Fatty acids, 4‐desmethylsterols, and triterpene alcohols from Tunisian lentisc (<i>Pistacia lentiscu</i>s) fruits
Bibliographic record
Abstract
Abstract A comparative study was performed to determine the fatty acid, 4‐desmethylsterol and triterpenic alcohols compositions of three different Tunisian populations of Pistacia lentiscus fruit Rimel (RM), Korbous (KO), and Tebaba (TB). Fruits are rich in lipids, which varied from 39.37% (KO) to 42.48% (TB) on a dry weight basis. Qualitatively, fatty acid, sterol, mono‐ and dimethylsterol composition is identical for all populations. Oleic acid was the major fatty acid for all samples, accounting from 40.49% in TB population to 50.72% in RM population followed by the palmitic and linoleic acids. Other fatty acids are present at lower levels. Total sterol amount varied from 109.72 mg/100 g of oil (KO) to 434.26 mg/100 g of oil (RM) with an average of 248.74 mg/100 g of oil. The major 4‐desmethylsterol component in all studied Tunisian populations of P. lentiscus oil was ß‐sitosterol followed by campesterol in TB and KO, and by stigmasterol in RM. The amount of total triterpenic alcohols varied from 42.39 mg/100 g of oil in RM population to 70.41 mg/100 g oil in TB population. The quantitative difference in the fatty acids and 4‐desmethylsterols of the different populations studied could be due to the effect of geographic region and soil type.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".